
"""simple docstring"""






from __future__ import annotations

from statistics import mean




def __magic_name__	(				_lowerCamelCase:		list[int],					_lowerCamelCase:		list[int],					_lowerCamelCase:		int			)					->						list[int]:

	'''simple docstring'''




	lowerCAmelCase												=  [0] * no_of_processes
	lowerCAmelCase												=  [0] * no_of_processes

	# Initialize remaining_time to waiting_time.

	for i in range(_lowerCamelCase			):
		lowerCAmelCase												=  burst_time[i]
	lowerCAmelCase												=  []

	lowerCAmelCase												=  0
	lowerCAmelCase												=  0

	# When processes are not completed,
	# A process whose arrival time has passed \
	# and has remaining execution time is put into the ready_process.
	# The shortest process in the ready_process, target_process is executed.

	while completed != no_of_processes:
		lowerCAmelCase												=  []
		lowerCAmelCase												=  -1

		for i in range(_lowerCamelCase			):
			if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
				ready_process.append(_lowerCamelCase			)

		if len(_lowerCamelCase			) > 0:
			lowerCAmelCase												=  ready_process[0]
			for i in ready_process:
				if remaining_time[i] < remaining_time[target_process]:
					lowerCAmelCase												=  i
			total_time += burst_time[target_process]
			completed += 1
			lowerCAmelCase												=  0
			lowerCAmelCase												=  (
			    total_time - arrival_time[target_process] - burst_time[target_process]
			)
		else:
			total_time += 1

	return waiting_time




def __magic_name__	(				_lowerCamelCase:		list[int],					_lowerCamelCase:		int,					_lowerCamelCase:		list[int]			)					->						list[int]:

	'''simple docstring'''




	lowerCAmelCase												=  [0] * no_of_processes
	for i in range(_lowerCamelCase			):
		lowerCAmelCase												=  burst_time[i] + waiting_time[i]
	return turn_around_time


if __name__ == "__main__":
					print("""[TEST CASE 01]""")

					UpperCAmelCase        =				4
					UpperCAmelCase        =				[2, 5, 3, 7]
					UpperCAmelCase        =				[0, 0, 0, 0]
					UpperCAmelCase        =				calculate_waitingtime(arrival_time, burst_time, no_of_processes)
					UpperCAmelCase        =				calculate_turnaroundtime(
					    burst_time, no_of_processes, waiting_time
					)

					# Printing the Result
					print("""PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time""")
					for i, process_id in enumerate(list(range(1, 5))):
										print(
										    f"""{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t"""
										    f"""{waiting_time[i]}\t\t\t\t{turn_around_time[i]}"""
										)
					print(f"""\nAverage waiting time = {mean(waiting_time):.5f}""")
					print(f"""Average turnaround time = {mean(turn_around_time):.5f}""")




"""simple docstring"""






from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 			lowercase					( lowercase__		):
			lowercase        =     ['''image_processor''', '''tokenizer''']
			lowercase        =     '''BlipImageProcessor'''
			lowercase        =     ('''BertTokenizer''', '''BertTokenizerFast''')






			def __init__(self  :	Any      ,SCREAMING_SNAKE_CASE_  :	List[str]      ,SCREAMING_SNAKE_CASE_  :	Optional[int]			)       ->	Dict:





				"""simple docstring"""


				lowerCAmelCase												=  False
				super().__init__(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  self.image_processor






			def __call__(self  :	int      ,SCREAMING_SNAKE_CASE_  :	ImageInput = None      ,SCREAMING_SNAKE_CASE_  :	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,SCREAMING_SNAKE_CASE_  :	bool = True      ,SCREAMING_SNAKE_CASE_  :	Union[bool, str, PaddingStrategy] = False      ,SCREAMING_SNAKE_CASE_  :	Union[bool, str, TruncationStrategy] = None      ,SCREAMING_SNAKE_CASE_  :	Optional[int] = None      ,SCREAMING_SNAKE_CASE_  :	int = 0      ,SCREAMING_SNAKE_CASE_  :	Optional[int] = None      ,SCREAMING_SNAKE_CASE_  :	Optional[bool] = None      ,SCREAMING_SNAKE_CASE_  :	bool = False      ,SCREAMING_SNAKE_CASE_  :	bool = False      ,SCREAMING_SNAKE_CASE_  :	bool = False      ,SCREAMING_SNAKE_CASE_  :	bool = False      ,SCREAMING_SNAKE_CASE_  :	bool = False      ,SCREAMING_SNAKE_CASE_  :	bool = True      ,SCREAMING_SNAKE_CASE_  :	Optional[Union[str, TensorType]] = None      ,**SCREAMING_SNAKE_CASE_  :	str      ,)       ->	BatchEncoding:





				"""simple docstring"""


				if images is None and text is None:
					raise ValueError('''You have to specify either images or text.'''			)

				# Get only text
				if images is None:
					lowerCAmelCase												=  self.tokenizer
					lowerCAmelCase												=  self.tokenizer(
					    text=SCREAMING_SNAKE_CASE_      ,add_special_tokens=SCREAMING_SNAKE_CASE_      ,padding=SCREAMING_SNAKE_CASE_      ,truncation=SCREAMING_SNAKE_CASE_      ,max_length=SCREAMING_SNAKE_CASE_      ,stride=SCREAMING_SNAKE_CASE_      ,pad_to_multiple_of=SCREAMING_SNAKE_CASE_      ,return_attention_mask=SCREAMING_SNAKE_CASE_      ,return_overflowing_tokens=SCREAMING_SNAKE_CASE_      ,return_special_tokens_mask=SCREAMING_SNAKE_CASE_      ,return_offsets_mapping=SCREAMING_SNAKE_CASE_      ,return_token_type_ids=SCREAMING_SNAKE_CASE_      ,return_length=SCREAMING_SNAKE_CASE_      ,verbose=SCREAMING_SNAKE_CASE_      ,return_tensors=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_      ,)
					return text_encoding

				# add pixel_values
				lowerCAmelCase												=  self.image_processor(SCREAMING_SNAKE_CASE_      ,return_tensors=SCREAMING_SNAKE_CASE_			)

				if text is not None:
					lowerCAmelCase												=  self.tokenizer(
					    text=SCREAMING_SNAKE_CASE_      ,add_special_tokens=SCREAMING_SNAKE_CASE_      ,padding=SCREAMING_SNAKE_CASE_      ,truncation=SCREAMING_SNAKE_CASE_      ,max_length=SCREAMING_SNAKE_CASE_      ,stride=SCREAMING_SNAKE_CASE_      ,pad_to_multiple_of=SCREAMING_SNAKE_CASE_      ,return_attention_mask=SCREAMING_SNAKE_CASE_      ,return_overflowing_tokens=SCREAMING_SNAKE_CASE_      ,return_special_tokens_mask=SCREAMING_SNAKE_CASE_      ,return_offsets_mapping=SCREAMING_SNAKE_CASE_      ,return_token_type_ids=SCREAMING_SNAKE_CASE_      ,return_length=SCREAMING_SNAKE_CASE_      ,verbose=SCREAMING_SNAKE_CASE_      ,return_tensors=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_      ,)
				else:
					lowerCAmelCase												=  None

				if text_encoding is not None:
					encoding_image_processor.update(SCREAMING_SNAKE_CASE_			)

				return encoding_image_processor






			def 					UpperCAmelCase							(self  :	List[str]      ,*SCREAMING_SNAKE_CASE_  :	str      ,**SCREAMING_SNAKE_CASE_  :	str			)       ->	Union[str, Any]:





				"""simple docstring"""


				return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	List[Any]      ,*SCREAMING_SNAKE_CASE_  :	str      ,**SCREAMING_SNAKE_CASE_  :	Optional[int]			)       ->	Any:





				"""simple docstring"""


				return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)






			@property
			def 					UpperCAmelCase							(self  :	Dict			)       ->	Tuple:





				"""simple docstring"""


				lowerCAmelCase												=  self.tokenizer.model_input_names
				lowerCAmelCase												=  self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)


